1. Technical Field
Aspects of the present disclosure relate to driver assistance systems in motorized vehicles. Specifically, the present disclosure includes methods for detecting and recognizing traffic signs using a driver assistance system (DAS) which includes a camera and an image processor mountable in a moving vehicle. The camera captures a series of images of the vehicle environment, e.g. of the road in front of the moving vehicle
2. Description of Related Art
Traffic sign recognition may be based on the characteristic shapes and colors of the traffic signs rigidly positioned relative to the environment situated in clear sight of the driver.
Known techniques for traffic sign recognition utilize at least two steps, one aiming at detection, and the other one at classification, that is, the task of mapping the image of the detected traffic sign into its semantic category. Regarding the detection problem, several approaches have been proposed. Some of these approaches rely on gray scale data of images. One approach employs a template based technique in combination with a distance transform. Another approach utilizes a measure of radial symmetry and applies it as a pre-segmentation within the framework. Since radial symmetry corresponds to a simplified (i.e., fast) circular Hough transform, it is particularly applicable for detecting possible occurrences of circular signs.
Other techniques for traffic sign detection use color information. These techniques share a two step strategy. First, a pre-segmentation is employed by a thresholding operation on a color representation, such as Red Green Blue (RGB). Linear or non-linear transformations of the RGB representation have been used as well. Subsequently, a final detection decision is obtained from shape based features, applied only to the pre-segmented regions. Corner and edge features, genetic algorithms and template matching have been used.
A joint approach for detection based on color and shape has also been proposed, which computes a feature map of the entire image frame, based on color and gradient information, while incorporating a geometric model of signs.
For the classification task, most approaches utilize well known techniques, such as template matching, multi-layer perceptrons, radial basis function networks, and Laplace kernel classifiers. A few approaches employ a temporal fusion of multiple frame detection to obtain a more robust overall detection.
US patent application publication 2006/0034484 discloses a method for detecting and recognizing a traffic sign. A video sequence having image frames is received. One or more filters are used to measure features in at least one image frame indicative of an object of interest. The measured features are combined and aggregated into a score indicating possible presence of an object. The scores are fused over multiple image frames for a robust detection. If a score indicates possible presence of an object in an area of the image frame, the area is aligned with a model. A determination is then made as to whether the area indicates a traffic sign. If the area indicates a traffic sign, the area is classified into a particular type of traffic sign. The present invention is also directed to training a system to detect and recognize traffic signs.
U.S. Pat. No. 6,813,545 discloses processes and devices which recognize, classify and cause to be displayed traffic signs extracted from images of traffic scenes. The processes analyze the image data provided by image sensors without any pre-recognition regarding the actual scenario. The terms “driver assistance system” and “vehicle control system” are used herein interchangeably. The term “driver assistance function” refers to the process or service provided by the “driver assistance system”. The terms “camera” and “image sensor” are used herein interchangeably. The term “host vehicle” as used herein refers to the vehicle on which the driver assistance system is mounted.